{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用线性SVM对鸢尾花数据进行分类操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [svm.SVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## 设置属性防止中文乱码\n",
    "mpl.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## 读取数据\n",
    "# 'sepal length', 'sepal width', 'petal length', 'petal width'\n",
    "iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'\n",
    "path = './datas/iris.data'  # 数据文件路径\n",
    "data = pd.read_csv(path, header=None)\n",
    "x, y = data[list(range(4))], data[4]\n",
    "y = pd.Categorical(y).codes # 把文本数据进行编码，比如abc编码为012\n",
    "x = x[[0, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "## 数据分割\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=28, train_size=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# svm.SVC API说明：\n",
    "# 功能：使用SVM分类器进行模型构建\n",
    "# 参数说明：\n",
    "# C: 误差项的惩罚系数，默认为1.0；一般为大于0的一个数字，C越大表示在训练过程中对于总误差的关注度越高，也就是说当C越大的时候，对于训练集的表现会越好，\n",
    "# 但是有可能引发过度拟合的问题(overfiting)\n",
    "# kernel：指定SVM内部函数的类型，可选值：linear、poly、rbf、sigmoid、precomputed(基本不用，有前提要求，要求特征属性数目和样本数目一样)；默认是rbf；\n",
    "# degree：当使用多项式函数作为svm内部的函数的时候，给定多项式的项数，默认为3\n",
    "# gamma：当SVM内部使用poly、rbf、sigmoid的时候，核函数的系数值，当默认值为auto的时候，实际系数为1/n_features\n",
    "# coef0: 当核函数为poly或者sigmoid的时候，给定的独立系数，默认为0\n",
    "# probability：是否启用概率估计，默认不启动，不太建议启动\n",
    "# shrinking：是否开启收缩启发式计算，默认为True\n",
    "# tol: 模型构建收敛参数，当模型的的误差变化率小于该值的时候，结束模型构建过程，默认值:1e-3\n",
    "# cache_size：在模型构建过程中，缓存数据的最大内存大小，默认为空，单位MB\n",
    "# class_weight：给定各个类别的权重，默认为空\n",
    "# max_iter：最大迭代次数，默认-1表示不限制\n",
    "# decision_function_shape: 决策函数，可选值：ovo和ovr，默认为None；推荐使用ovr；（1.7以上版本才有）\n",
    "# '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 数据SVM分类器构建\n",
    "clf = svm.SVC(C=1, kernel='linear')\n",
    "\n",
    "## 模型训练\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.822222222222\n",
      "训练集准确率： 0.822222222222\n",
      "0.783333333333\n",
      "测试集准确率： 0.783333333333\n",
      "decision_function:\n",
      " [[-0.11664996  2.1037268   1.01292316]\n",
      " [ 2.08483365 -0.07808377  0.99325012]\n",
      " [-0.11390704  2.11671179  0.99719525]\n",
      " [ 2.12333877  1.00956314 -0.1329019 ]\n",
      " [-0.30410641  1.0680337   2.23607271]\n",
      " [ 2.20066154  0.90242835 -0.10308989]\n",
      " [ 2.23642373  0.97709028 -0.21351401]\n",
      " [ 2.12333877  1.00956314 -0.1329019 ]\n",
      " [ 1.02396842  2.10696091 -0.13092934]\n",
      " [-0.32351524  1.16542455  2.1580907 ]\n",
      " [ 2.11785292  0.98359317 -0.10144609]\n",
      " [-0.07529774  2.11021583  0.96508191]\n",
      " [-0.12213581  2.07775684  1.04437897]\n",
      " [ 2.23093789  0.95112031 -0.18205819]\n",
      " [-0.22688782  1.05504178  2.17184604]\n",
      " [-0.08078359  2.08424586  0.99653773]\n",
      " [ 2.13441464  0.96736021 -0.10177485]\n",
      " [-0.03932719  0.99659202  2.04273517]\n",
      " [ 2.0765007   0.97710414 -0.05360484]\n",
      " [ 2.17850979  0.98683421 -0.165344  ]\n",
      " [-0.13046876  2.13294475  0.99752401]\n",
      " [-0.02835549  2.04853196  0.97982354]\n",
      " [ 2.06806357  1.12643492 -0.19449849]\n",
      " [-0.06422186  2.0680129   0.99620897]\n",
      " [-0.33986861  0.99337178  2.34649683]\n",
      " [-0.11380286  1.02256892  2.09123393]\n",
      " [-0.15800218  2.09723778  1.0607644 ]\n",
      " [-0.09185947  2.12644879  0.96541067]\n",
      " [ 2.19232859  0.95761627 -0.14994486]\n",
      " [-0.15525926  2.11022276  1.04503649]\n",
      " [-0.17730683  2.10048576  1.07682107]\n",
      " [-0.24629665  2.15243263  1.09386402]\n",
      " [ 0.98535913  2.11345687 -0.098816  ]\n",
      " [-0.16897388  1.04529784  2.12367603]\n",
      " [-0.19661148  1.10373374  2.09287774]\n",
      " [ 2.17028102  0.94787926 -0.11816028]\n",
      " [-0.07793648  1.00308798  2.07484851]\n",
      " [ 2.0765007   0.97710414 -0.05360484]\n",
      " [-0.31792521  1.09725165  2.22067357]\n",
      " [-0.12487873  1.06477185  2.06010688]\n",
      " [-0.4337531   1.11673952  2.31701358]\n",
      " [-0.2131732   2.1199667   1.0932065 ]\n",
      " [-0.37309624  1.11998057  2.25311567]\n",
      " [-0.2241449   1.06802677  2.15611813]\n",
      " [-0.10557409  2.06152387  1.04405021]\n",
      " [ 2.19781444  0.98358623 -0.18140067]\n",
      " [ 2.0874724   1.02904408 -0.11651647]\n",
      " [ 2.17850979  0.98683421 -0.165344  ]\n",
      " [ 2.12618587 -0.07159475  0.94540887]\n",
      " [-0.23247785  1.12321468  2.10926317]\n",
      " [-0.26001127  1.08750771  2.17250356]\n",
      " [ 2.15097637  0.95112724 -0.10210361]\n",
      " [-0.1993544   1.09074875  2.10860565]\n",
      " [-0.08626944  2.05827589  1.02799354]\n",
      " [-0.2241449   1.06802677  2.15611813]\n",
      " [-0.2379637   1.09724471  2.14071898]\n",
      " [-0.16623095  1.05828283  2.10794813]\n",
      " [-0.10842119  2.14268175  0.96573943]\n",
      " [-0.20209733  1.07776377  2.12433356]\n",
      " [ 1.0074067   2.12319388 -0.13060058]\n",
      " [-0.12487873  1.06477185  2.06010688]\n",
      " [-0.11664996  2.1037268   1.01292316]\n",
      " [-0.24629665  2.15243263  1.09386402]\n",
      " [-0.30125931  0.98687582  2.31438349]\n",
      " [-0.16074511  1.0842528   2.07649231]\n",
      " [-0.29313472  1.11997364  2.17316108]\n",
      " [ 2.20066154  0.90242835 -0.10308989]\n",
      " [-0.11664996  2.1037268   1.01292316]\n",
      " [-0.26275419  1.07452273  2.18823147]\n",
      " [ 2.18684274  0.9316463  -0.11848904]\n",
      " [ 2.15371929  0.96411223 -0.11783152]\n",
      " [-0.23785952  1.00310185  2.23475767]\n",
      " [ 2.11511     0.97060818 -0.08571818]\n",
      " [-0.20209733  1.07776377  2.12433356]\n",
      " [-0.27667717  2.19788354  1.07879364]\n",
      " [-0.07814485  2.19137371  0.88677114]\n",
      " [ 2.12608169  1.02254812 -0.14862981]\n",
      " [-0.5         1.18167138  2.31832862]\n",
      " [-0.33722986  1.10049963  2.23673024]\n",
      " [ 2.15371929  0.96411223 -0.11783152]\n",
      " [-0.07804067  2.09723084  0.98080982]\n",
      " [ 2.18673856  1.02578916 -0.21252773]\n",
      " [ 2.12608169  1.02254812 -0.14862981]\n",
      " [-0.2241449   1.06802677  2.15611813]\n",
      " [-0.2794201   2.18489855  1.09452154]\n",
      " [ 2.18125271  0.9998192  -0.18107191]\n",
      " [ 2.23104207 -0.14302256  0.91198049]\n",
      " [ 2.26406133  0.91865438 -0.18271572]\n",
      " [ 2.15930932 -0.10406067  0.94475135]\n",
      " [ 2.10677704  1.0257961  -0.13257314]]\n",
      "\n",
      "predict:\n",
      " [1 0 1 0 2 0 0 0 1 2 0 1 1 0 2 1 0 2 0 0 1 1 0 1 2 2 1 1 0 1 1 1 1 2 2 0 2\n",
      " 0 2 2 2 1 2 2 1 0 0 0 0 2 2 0 2 1 2 2 2 1 2 1 2 1 1 2 2 2 0 1 2 0 0 2 0 2\n",
      " 1 1 0 2 2 0 1 0 0 2 1 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "## 计算模型的准确率/精度\n",
    "print (clf.score(x_train, y_train)) \n",
    "print ('训练集准确率：', accuracy_score(y_train, clf.predict(x_train)))\n",
    "print (clf.score(x_test, y_test))\n",
    "print ('测试集准确率：', accuracy_score(y_test, clf.predict(x_test)))\n",
    "\n",
    "## 计算决策函数的结构值以及预测值(decision_function计算的是样本x到各个分割平面的距离<也就是决策函数的值>)\n",
    "print ('decision_function:\\n', clf.decision_function(x_train))\n",
    "print ('\\npredict:\\n', clf.predict(x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5JBYdHW6DQOyfzItlMjcG+W6JjcjISGrUqMH4iPFWe/sk/nI8fsX8rHKuvEbm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\n5Rc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29jg4OODg4MDixYvp0KED77//Pjqdjq5du/LJJ5+Yvm9vb0/Xrl3zHWdwcDC9evWi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\nqGzjiDFRQ1IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb35a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "N = 500\n",
    "x1_min, x2_min = x.min()\n",
    "x1_max, x2_max = x.max()\n",
    "\n",
    "t1 = np.linspace(x1_min, x1_max, N)\n",
    "t2 = np.linspace(x2_min, x2_max, N)\n",
    "x1, x2 = np.meshgrid(t1, t2)  # 生成网格采样点\n",
    "grid_show = np.dstack((x1.flat, x2.flat))[0] # 测试点\n",
    "\n",
    "\n",
    "grid_hat = clf.predict(grid_show)       # 预测分类值\n",
    "grid_hat = grid_hat.reshape(x1.shape)  # 使之与输入的形状相同\n",
    "\n",
    "cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])\n",
    "cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])\n",
    "plt.figure(facecolor='w')\n",
    "## 区域图\n",
    "plt.pcolormesh(x1, x2, grid_hat, cmap=cm_light)\n",
    "## 所以样本点\n",
    "plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)      # 样本\n",
    "## 测试数据集\n",
    "plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)     # 圈中测试集样本\n",
    "## lable列表\n",
    "plt.xlabel(iris_feature[0], fontsize=13)\n",
    "plt.ylabel(iris_feature[1], fontsize=13)\n",
    "plt.xlim(x1_min, x1_max)\n",
    "plt.ylim(x2_min, x2_max)\n",
    "plt.title(u'鸢尾花SVM特征分类', fontsize=16)\n",
    "plt.grid(b=True, ls=':')\n",
    "plt.tight_layout(pad=1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
